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Radiology-artificial Intelligence

SCIE

国际简称：RADIOL-ARTIF INTELL 参考译名：放射科-人工智能

中科院分区
1区
CiteScore分区
Q1
JCR分区
Q1

基本信息：: ISSN：2638-6100; 是否OA：未开放; 是否预警：否; TOP期刊：是

出版信息：: 出版商：Radiological Society of North America Inc.; 研究方向：Multiple

评价信息：: 影响因子：20.1; CiteScore指数：21.1; SJR指数：4.61; SNIP指数：4.468

发文数据：: Gold OA文章占比：17.39%; 研究类文章占比：98.44%; 年发文量：64

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英文简介期刊介绍 CiteScore数据中科院SCI分区 JCR分区发文数据常见问题

英文简介Radiology-artificial Intelligence期刊介绍

Radiology: Artificial Intelligence is an international academic journal dedicated to the application of artificial intelligence in the field of radiology. This journal aims to provide a platform for researchers in radiology, computer science, biomedical engineering, and related fields to showcase and exchange the latest research achievements on artificial intelligence technology in medical imaging diagnosis, analysis, and processing.

The magazine covers a wide range of disciplinary topics, including but not limited to deep learning in medical imaging, machine learning, computer vision, image reconstruction, image segmentation, disease detection and classification, radiomics, and the application of artificial intelligence in radiology practice. The journal places special emphasis on emerging research fields and encourages interdisciplinary research methods to address the complex challenges faced in the field of medical imaging. Through its open access model, this journal helps promote knowledge sharing and scientific progress, playing an important role in fostering collaboration and innovation in the global scientific community.

期刊简介Radiology-artificial Intelligence期刊介绍

《Radiology-artificial Intelligence》是一本医学优秀杂志。致力于发表原创科学研究结果，并为医学各个领域的原创研究提供一个展示平台，以促进医学领域的的进步。该刊鼓励先进的、清晰的阐述，从广泛的视角提供当前感兴趣的研究主题的新见解，或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道医学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单，目前已被权威数据库SCIE收录，得到了广泛的认可。

该期刊投稿重要关注点：

国际TOP期刊
医学
计算机人工智能
SCIE
中科院1区
非预警

Cite Score数据（2026年6月最新版）Radiology-artificial Intelligence Cite Score数据

CiteScore：21.1
SJR：4.61
SNIP：4.468

学科类别	分区	排名	百分位
大类：Medicine 小类：Radiology, Nuclear Medicine and Imaging	Q1	4 / 361	99%
大类：Medicine 小类：Radiological and Ultrasound Technology	Q1	2 / 64	97%
大类：Medicine 小类：Artificial Intelligence	Q1	26 / 570	95%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础，针对的是前四年发表的论文的引文。CiteScore的意义在于，它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法，而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Radiology-artificial Intelligence 中科院分区

《新锐期刊分区表》（2026年3月发布） 综述期刊：否 Top期刊：是

大类学科	分区	小类学科	分区
医学	1区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 核医学	1区 1区

期刊分区表（2025年3月升级版） 综述期刊：否 Top期刊：是

大类学科	分区	小类学科	分区
医学	1区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 核医学	1区 2区

中科院分区表 是以客观数据为基础，运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据，得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次，类似于“优、良、及格”等。最开始，这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Radiology-artificial Intelligence JCR分区

2025-2026年最新版

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	5 / 210	97.9
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	SCIE	Q1	1 / 217	99.8
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	7 / 210	96.9
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	SCIE	Q1	3 / 217	98.85

2023-2024年最新版

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	ESCI	Q1	21 / 197	89.6
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	ESCI	Q1	9 / 204	95.8
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	ESCI	Q1	20 / 198	90.15
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	ESCI	Q1	12 / 204	94.36

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异，此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此，JCR将期刊按照学科门类和影响因子分为不同的分区，这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

本刊中国学者近年发表论文

1、Quantifying the Privacy-Utility Trade-off in Medical Imaging A
Author: Yu, Zekai

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 2, pp. -. DOI: 10.1148/ryai.251044
2、Quantifying the Privacy-Utility Trade-off in Medical Imaging AI The Respons
Author: Yu, Zekai

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 2, pp. -. DOI:
3、Adnexal Lesion Discrimination Using Deep Learning Analysis of Dynamic Contrast-enhanced US Image
Author: Wu, Manli; Yang, Hong; Chen, Ying; Wu, Shuangyu; Liang, Tianming; Zhang, Man; Qu, Enze; Sun, Xiaofeng; Zhang, Rui; Mu, Liang; Xiao, Li; Wen, Hong; Wang, Ruili; Liu, Tingting; Meng, Xiaotao; Su, Manting; Wang, Ying; Gu, Jian; Chen, Sijia; Wang, Yaping; Zhao, Qinghong; Liu, Juan; Cheng, Ping; Wang, Ruixuan; Hu, Jianfang; Zhang, Xinling

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.240786
4、Deep Learning for Coronary Stenosis Detection in Heavily Calcified Plaques at Coronary CT Angiography: A Stepwise, Multicenter Stud
Author: Wang, Rui; Wang, Siwen; Zhang, Libo; Schoepf, U. Joseph; Zhang, Fandong; Chen, Wei; Zhou, Zhen; Fang, Zhe; Hu, Bin; Yu, Yizhou; Zhang, Jiayin; Wang, Ximing; Zhang, Longjiang; Xu, Lei

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.250109
5、Visualizing Radiologic Connections: An Explainable Coarse-to-Fine Foundation Modelwith Multiview Mammograms and Associated Report
Author: Gao, Yuan; Zhou, Hong-Yu; Wang, Xin; Portaluri, Antonio; Zhang, Tianyu; Beets-Tan, Regina; Han, Luyi; Lu, Chunyao; Estacio, Laura; Da'ngelo, Anna; Ursprung, Stephan; Yu, Yizhou; Teuwen, Jonas; Tan, Tao; Mann, Ritse

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.240646
6、Gastric Neoplasm Detection at Contrast-enhanced CT with Deep Learnin
Author: Chen, Xin; Xia, Yingda; Yao, Lisha; Li, Suyun; Liang, Yanting; Zheng, Zhilin; Yuan, Mingze; Yao, Jiawen; Zhang, Ruiping; Tu, Wenting; Guo, Yongmei; Liang, Dan; Ma, Zelan; Chen, Dandan; Lai, Lisha; Xie, Xiaowen; Yu, Yifan; Jia, Yanlian; Zhang, Ling; Liu, Zaiyi

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.1148/ryai.250145
7、Pseudo-Contrast-enhanced US via Enhanced Generative Adversarial Networks for Evaluating Tumor Ablation Efficac
Author: Chen, Chen; Yu, Jiabin; Xu, Zhikang; Xu, Changsong; Zhou, Zubang; Hao, Jindong; Wang, Vicky Yang; Yao, Jincao; Zhou, Lingyan; Xu, Chenke; Song, Mei; Zhang, Qi; Liu, Xiaofang; Sui, Lin; Yan, Yuqi; Jiang, Tian; Zhou, Yahan; Wu, Yingtianqi; Xiao, Binggang; Xu, Chenjie; Mi, Hongmei; Yang, Li; Wu, Zhiwei; He, Qingquan; Chen, Jian; Liu, Qi; Xu, Don

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2025; Vol. 7, Issue 3, pp. -. DOI: 10.1148/ryai.240370
8、Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosi
Author: Tang, Tianyu; Cui, Ying; Lu, Chunqiang; Li, Huiming; Zhou, Jiaying; Zhang, Xiaoyu; Zhou, Yujie; Zhang, Ying; Zhang, Yi; Xu, Yuhao; Li, Yuefeng; Ju, Shenghong

Journal: RADIOLOGY-ARTIFICIAL INTELLIGENCE. 2025; Vol. 7, Issue 3, pp. -. DOI: 10.1148/ryai.240072